Method and system for anticipatory deployment of autonomously controlled vehicles

ABSTRACT

A method and system for management and anticipatory deployment of autonomously controlled vehicles are disclosed. According to one embodiment, a method may include calculating the geographic locations and periods of time where self-driving vehicles might experience the greatest probability of being requested to provide transportation services to passengers or cargo, and then communicating the resulting locations and times to self-driving vehicles, causing the vehicles to deploy themselves to those certain locations at those certain times, all prior to and in anticipation of specific requests being initiated by users or entities for such transport.

RELATED APPLICATIONS

This application claims priority to U.S. application Ser. No.15/990,732, filed on May 28, 2018, which is assigned to the assignee ofthe present application, which application is a divisional applicationof U.S. application Ser. No. 14/601,167, which is assigned to theassignee of the present application and was filed on Jan. 20, 2015issued on May 29, 2018 as U.S. Pat. No. 9,984,574, which applicationclaims priority to provisional application No. 61/929,776 filed on Jan.21, 2014.

FIELD OF THE INVENTION

This invention relates to logistical systems for managing thetransportation of people and goods, and more specifically to a computerbased system for the management of the deployment logistics oftransportation resources for servicing human passengers and other cargo.

BACKGROUND

Autonomous vehicles that are capable of operation without humanintervention are rapidly improving. As such vehicles are commercializedthey will improve local transportation by providing greaterfunctionality and allowing new methods and systems to be utilized formoving passengers and cargo. One such enabling-functionality is theability of the vehicle to move from one location to another locationwhile empty of any human. For transportation logistics this manifestsitself as the attribute of being able to self-deploy, that is move fromone user to the next user discontinuously and autonomously. This abilityto self-deploy permits broad changes to occur in the methods and systemsof local transportation. When self-deployable vehicles arecommercialized, existing methods and systems of personal and commercetransportation will be utilized in new ways, and innovative new methodsand systems of personal transportation and commerce will be developed.Multiple technologies ancillary to the self driving vehicle have beendeveloped that are essential to the efficient operation of thetransportation systems they may be used in, examples of which are shownin the following US patents and publications: communications (U.S. Pat.Nos. 7,064,681 6,697,730 7,482,952B2 US20110059693 US20130132887US20140011521), tracking (U.S. Pat. Nos. 7,479,901 7,538,691US20140129135), mapping, (U.S. Pat. No. 6,356,838), and specifically fortechnologies involved in the distribution logistics for managing thedeployment of vehicles such as routing drivers and positioning ofpassengers (U.S. Pat. No. 6,356,838; US20110059693; US20120239452A1;US20130132140; US20130204676; US20140011522A1).

There is a need for improved systems and methods to enable greaterutilization of self-driving vehicles. One method for optimizing systemperformance is through ride sharing, such as described in U.S. Pat. No.7,840,427. However there remains a need for systems and methods thatimprove the cost and efficiency of self-driving vehicles. The economicbenefits of such systems and methods in addition to the benefitsself-driving vehicles promise to offer in safety, productivity, fuelconsumption, and carbon emissions.

SUMMARY

Various embodiments of a method and system for anticipatory deploymentof autonomously controlled vehicles are disclosed. As used herein, theterms “autonomously controlled”, “self-driving”, “self-deploying” andvariants thereof when used in describing a vehicle refer to a vehiclewith capabilities as specified in the National Highway Traffic SafetyAdministration (NHTSA) definitions for vehicle automation, andspecifically Level 4 of the NHTSA definitions, “Full Self-DrivingAutomation (Level 4): The vehicle is designed to perform allsafety-critical driving functions and monitor roadway conditions for anentire trip. Such a design anticipates that the driver will providedestination or navigation input, but is not expected to be available forcontrol at any time during the trip. This includes both occupied andunoccupied vehicles.” U.S. Department of Transportation Releases Policyon Automated Vehicle Development, NHTSA 14-13, Thursday, May 30, 2013.Embodiments disclosed herein modify the foregoing definition byproviding for the vehicle the destination or navigation input, which isdetermined in a manner as described herein.

According to one embodiment, calculating and then communicating data toa self-driving vehicle causing the vehicle to deploy itself to a certainlocation at a certain time, where the data has been calculated so thatthe vehicle might realize the greatest probability of being needed forproviding transportation services to a passenger or cargo, all prior toand in anticipation of a specific request being initiated by any user orentity for such transport.

A system is further contemplated that in one embodiment may include afirst and a second computer system. The first computer system may beconfigured to identify destination geographical areas and times to whichto pre-deploy autonomously controlled vehicles. The second computersystem may be configured to communicate with the first computer systemvia a network, with the second computer managing the dynamic flow ofautonomously controlled vehicles as they carry passengers. As passengersarrive at destinations and vehicles become free of passengers, the firstcomputer system may convey complete specifications of optimumanticipatory deployment instructions to the second computer system.

Additional aspects related to the invention will be set forth in part inthe description which follows, and in part will be apparent to thoseskilled in the art from the description, or may be learned by practiceof the invention. Aspects of the invention may be realized and attainedby means of the elements and combinations of various elements andaspects particularly pointed out in the following detailed descriptionand the appended claims.

It is to be understood that both the foregoing and the followingdescriptions are exemplary and explanatory only and are not intended tolimit the claimed invention or application thereof in any mannerwhatsoever.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification exemplify the embodiments of the presentinvention and, together with the description, serve to explain andillustrate principles of the inventive techniques. Specifically:

FIGS. 1(a) and 1(b) illustrate one embodiment of an anticipatorydeployment system for self-driving vehicles by means of a computergenerated matrix of anticipatory vehicle demand values calculated byprobability by a computer and using a city street map and a grid patternof some designs.

FIG. 2 illustrates a network of computers and communication devices thatcan be used to operate and execute an anticipatory deployment system.

FIGS. 3(a), 3(b) and 3(c) illustrate how data on a user's trip can bestored within a three-dimensional matrix comprised of a specified area(square on the grid) at different specified times.

FIGS. 4(a) and 4(b) illustrate creation of a three-dimensional matrix ofsegregated areas by segregated times.

FIGS. 5(a), 5(b), 5(c) and 5(d) illustrate operation of one embodimentof an anticipatory deployment system for self driving vehicles whereavailable vehicles are anticipatorily deployed based on anticipated userrequests

FIGS. 6(a), 6(b), 6(c) and 6(d) illustrate various deployment scenariosof a self-deploying vehicle in an anticipatory deployment system.

FIG. 7 illustrates an example of multiple vehicles in an anticipatorydeployment system.

FIG. 8 illustrates an example user interface on a mobile device in ananticipatory deployment system.

FIGS. 9(a) and 9(b) illustrate a flow chart showing a process that isused when a user interacts in an anticipatory deployment system.

DETAILED DESCRIPTION

In the following detailed description, reference will be made to theaccompanying drawing(s), in which identical functional elements aredesignated with like numerals. The aforementioned accompanying drawingsshow by way of illustration, and not by way of limitation, specificembodiments and implementations consistent with principles of thepresent invention. These implementations are described in sufficientdetail to enable those skilled in the art to practice the invention andit is to be understood that other implementations may be utilized andthat structural changes and/or substitutions of various elements may bemade without departing from the scope and spirit of present invention.The following detailed description is, therefore, not to be construed ina limited sense.

Computerized methods and systems are disclosed herein for deliveringintelligently calculated logistical information to user(s) and owner(s)about where their self-deploying vehicle(s) should be anticipatorilydeployed in order to better effectively serve the transportation needsof the user/owners. This is distinctly different from providingtransportation services on demand to paying customers. Using heuristiclogic in combination with other input data in order to determine theoptimum anticipatory deployment of self-driving vehicles by their ownersand fractional owners is a different and distinct technical approachthan distributing for-hire taxis or limousines. Modeling location andtime values to facilitate the positioning of vehicles most economicallyfor anticipated future needs of owners, and, creating an onlinemarketplace where owners choose among market generated transportationcost valuations is a novel technical approach that injects marketpricing into the system, allowing for consumer choice amongst selectedproviders and thus driving system costs down.

Current on-demand services, such as fleet management systems employedfor taxi and limousine fleets, typically utilize onboard meteringdevices, radios, and cell phones to dispatch drivers and monitor fares.Some companies have recently been developing online management systemsand system components for processing user requests for transportationservices and providing those services for a fee, where the management ofthe system occurs on a remote computer or computers and where usersinterface on mobile devices.

The technical methods and systems disclosed herein permit individualsand groups to plan and manage their use of self-deploying transportationservices including: participating in an on-line auction forcompetitively pricing transportation services; utilizing transportationfrom individually owned, shared ownership, and fractionally ownedvehicles; joining multiple ownership groups as needed; collaborating onconsumption behaviors with fellow user/owners to reduce costs; beingimmediately and tangibly financially rewarded for practicing behaviorsthat improve system performance; and participating in an onlineelectronic commerce marketplace for accounting and payment. The resultis a system that provides highly efficient and personalizedtransportation services that reduce cost by encouraging cost cuttingbehaviors, including financial rewards for cost cutting behaviors in thesystem, and by calculating anticipatory deployment data.

By way of example, three families can each own one self-deployingvehicle, and share ownership of a fourth self-deploying vehicle. Thisfourth vehicle they share amongst them as an on-demand transportationresource. Ownership of the fourth vehicle is held jointly by the threefamilies, and each utilizes the fourth vehicle under terms they control.As a result of this system, when traveling locally each member of eachfamily has access to their own self-deploying car and the jointly-ownedfourth vehicle, increasing the likelihood that a vehicle will beavailable as needed and likely decreasing the distance theself-deploying vehicle travels between passengers. The terms “own”,“owner” or “ownership” and other variants as used herein refer generallyto any arrangement that permits usage of a vehicle, such as by holdingtitle to the vehicle, or renting or leasing the vehicle.

The management of this method of self-deployment of fleets ofself-driving vehicles provides for new economic efficiency to berealized. Computer modeling of historical data of transportation use andbehaviors, and a multitude of other input variables is used toanticipate the transportation needs within a geographical area, andassign numerical probabilities of anticipated use needs by geographiclocation and time. By modeling these use patterns, groups ofself-deploying vehicles can be anticipatorily deployed in such a manneras to increase the likelihood of being pre-positioned in closestproximity to where and when a user will need transportation services,thus lowering the delivery costs of providing these transportationservices. Additional input variables may be used in the modeling foranticipatory deployment, including data on traffic, weather, humanevents, and other data regarding any such input variable as the systemdesigners may deem relevant. A computer system that models customerbehaviors to best predict where and when a customer is most likely toneed transportation allows owners to pre-position their vehiclesaccordingly, thus reducing the amount of time and distance a vehiclemust travel in order to get to the next user and thereby reducing thecost of operating the group of vehicles.

FIGS. 1(a-b) illustrate one embodiment of a method and system for ananticipatory deployment system for self driving vehicles by means of acomputer generated matrix of anticipatory vehicle demand valuescalculated by probability by a computer and using a city street map anda grid pattern of some designs. Using the method and system depicted inFIGS. 1(a-b), a variety of historical data on trips taken by any user,including but not limited to times, routes, frequency, priority ofimportance to user, and other relevant preferences or variables, can beentered into a computer database and used to calculate future demandprobabilities by location and time frames for that user. Groupingscomprising any number of users can be created in one database and thedata used to calculate a net-sum of probabilities by zone for the groupthat a trip will be initiated by an individual within any given zone(time and location), and self-deploying vehicles can be anticipatorilydeployed accordingly. Such an algorithm may incorporate the use ofweighted values and estimated values for any number of variablesincluding but not limited to frequency of prior travel, and may bedesigned to optimize the efficiency of the system including cost.

FIGS. 1(a-b) illustrate how probabilities that a vehicle might berequested for transportation will be calculated for sub-areas within abroader area so that a vehicle might be instructed to move closer inproximity to the area with the higher probability it will be needed sothat vehicles are instructed to deploy to more efficiently serveanticipated needs. In the example of FIG. 1(a) area A represents an areawhere the calculated probability that someone will need transportationat 8:15 pm is 44%. Area B represents an area where the calculatedprobability that someone will need transportation at 8:15 pm is 12%. Asseen in FIG. 1(b), since the probability of a user needing aself-deploying is higher in area A, a vehicle is anticipatorily deployedcloser to Point A than to Point B, improving system efficiencies andlowering system costs.

FIG. 2 illustrates a network of computers and communication devices thatcan be used to operate and execute a method and system for theanticipatory deployment of vehicles. The network includes a systemscomputer 202 for calculating values, processing information, and issuingdeployment instructions. The computer is illustrated generally and maytake the form of a general purpose computer or one with speciallydesigned hardware. The computer 202 communicates securely and wirelesslywith other system components via a wireless communication system showngenerally at 204. Users of the system may interact and enter and receivedata via a variety of computing devices including laptops or tabletssuch as shown at 206 or mobile devices such as shown at 208. The systemalso communicates with one or more self-deploying vehicles 210.

The methods and systems disclosed herein may include the creation of amatrix of values to enable the determination of the most efficient andcost effective locations and times for the deployment of self-deployingvehicles. The values represent the probability that a vehicle will berequired at a particular place and time. The values may also representeconomic efficiency of the system to reduce vehicle usage and/or cost toa particular user or to the system. The values may also representvehicle capabilities and services, for example the values may indicate apreference or need for a limousine or a vehicle of a particular size orthat has installed particular amenities such as a computer and screen,or a bar.

FIGS. 3(a-c) depict how a matrix of values is created by first dividinga physical area into sections, represented by the grids shown in FIGS.3(a-c). FIG. 3(b) illustrates how an individual trip might be recordedas data within the sections and FIG. 3(c) illustrates how that datamight be represented in a three-dimensional matrix when data on a tripis segregated by location and time, with data on any trip being recordedin multiple sections each representing a distinct time and location. Asseen in FIG. 3(a), a pattern of area sectioning is created to overlayany physical area where any transportation routes are used. The physicalarea is divided into sections that are arranged in any manner and of anysize. As seen in FIG. 3(b) data on individual trips through the physicalarea can be digitally stored as data within each section, including suchdata as time, travel direction, route, start and stop points, and otherrelevant information. A three-dimensional matrix of data containedwithin sections is created as shown in FIG. 3(c) by creating a new setof sections at regular time increments, so that a trip made by anindividual through the physical space is digitally recorded in segmentsidentifiable in both time and location.

FIGS. 4(a-b) illustrate creation of a three dimensional matrix ofsegregated areas by segregated times. As seen in FIGS. 4(a-b) a set ofsegregated areas and a set of segregated times can be constructed into athree-dimensional matrix of area and time, where each segmented area canbe assigned a unique coordinate, as illustrated area “A” can berepresented at three separate times as coordinates A1, A2, and A3.

The method and system depicted in FIGS. 3(a-c) allows a vehicle to bedigitally mapped for travel while occupied, and, mapped for the timesand locations where it is available for use. FIG. 5(a) is anillustration, employing the three dimensional matrix of area and timeexplained in connection with FIGS. 4(a-b). The illustration shown inFIG. 5(a) represents a 24-hour period where the dotted lines 502, 503represent when and where a vehicle is traveling while occupied, and theellipses 504, 505, 506 represent the times and locations when thevehicle is unoccupied and available for use. The three dimensionalellipse shapes are representative of the area in which a vehicle isavailable, starting in one location and time (left side of the ellipse),and then expanding outward over a wider area as time progresses, andthen contracting in area again as the time approaches the next locationand time where the vehicle is scheduled to provide transportation (rightedge of the ellipse). For example, the vehicle in the routes shown inFIG. 5(a) is available over an area that is fairly proximate to the endpoint of route 502 immediately following termination of route 502. Astime passes, the vehicle is able to be available in a progressivelylarger area until the mid-point in time between the termination of route502 and the initiation of route 503, at which point the area in whichthe vehicle is available gradually shrinks so that the vehicle isavailable at the initiation of route 503 at the designated startingpoint.

Using the same method of creating a matrix as depicted in FIGS. 3(a-c),as illustrated in FIG. 5(b), a user request for transportation servicescan be depicted as a starting point 507, an ending point 508, a route(dotted squiggly line 509), and a specific travel time (solid box 510)or a selected time window (dashed box 511) to travel.

Performed by computer, any specific user travel request may be matchedwith any vehicle that is available, as illustrated by FIG. 5(c) showinga match. After a match has been made and the vehicle committed to thetravel request, the system may calculate new values for the vehicle'savailability as illustrated in FIG. 5(d), and these new values used tocalculate new values for the anticipatory deployment of any othervehicles in the system. The system is dynamic so that when any userinitiates the occupancy of a vehicle for a trip it may re-position othervehicles in the system. This could occur, for example, when a user (whobelongs to owner group A and owner group secures a ride with a vehiclefrom group A. Since the probability of that user needing the same ridefrom group B would immediately drop to zero, and since the system knowsthis, the probability value for that user can be changed to zero in thematrix at group B, which might result in a group B vehicle moving to abetter deployment location.

Using the system depicted in FIG. 3, historical data on user travel canbe entered to construct a matrix of past travel data that can be used tocalculate the probability of future service requests, as depictedvisually in FIG. 6(a), where the lines 602, 603, 604 represent dataentered on past travel and the values represent the probability that thetrip will be requested based upon past frequency and other variables. Asseen in FIG. 6(a), the probability of trip 602 being requested is 28%,the probability of trip 603 being requested is 84% and the probabilityof trip 604 being requested is 57%.

For any individual vehicle, when the vehicle reaches its end point of anoccupied trip, the matrix of the probability of anticipated servicerequests as digitally constructed and as illustrated in FIG. 6(b) isused to instruct a self-deploying vehicle to deploy to a more systemefficient location represented visually as the star. FIG. 6(b) depicts aself-deploying vehicle with an occupant where the systems computer 202has calculated that the vehicle should relocate from end-point 605 ofthe trip 606 to a different location at a particular time, shown at 607,in order to place the vehicle in a more efficient location and time topotentially serve requests the system anticipates for trips 602, 603 and604. A self-deploying vehicle may be instructed to be anticipatorilydeployed by the system from location 605 to location 607 to be locatedmore proximately in time and location to trips 602, 603 and 604 that maybe requested by a user of the system.

FIG. 6(c) illustrates a self-deploying vehicle being deployed in onelocation, and then deployed to different position when a change in adata variable causes a change in the value of the potentiality ofanticipated user requests. Autonomously driven vehicles can be providedwith anticipatory deployment instructions so as to arrive withinproximity to any zone based upon calculations that include any number ofvariables (as may be defined by the system designers, programmers, andusers of any system, or, in response to a user request, or, user orgroup profile and settings, or, any other variable). In the illustrationseen in FIG. 6(c) a self-deploying vehicle is visualized being deployedin one location, and then deployed to different position when a changein a data variable causes a change in the value of the potentiality ofanticipated user requests. Such anticipatory deployment adjustmentsmight be caused by any variation in the data used to calculate a currentdeployment, including but not limited to:

a. The value of transportation services based upon the fractional costof ownership and the direct variable costs for each rider on every trip.

b. User's preferences, individually or collectively, the preferences toinclude the type and kind of transportation, the level of socialinteraction and privacy.

c. Current and anticipated human and natural events to predict demandfor services, including weather events, sporting events, normalcommuting, and any other event that may affect demand.

d. Routing data obtained from user defined routing data generatedthrough a user interface on a digital media including user definedrouting data created to facilitate route preferences, ride sharing,flexible scheduling, and other user services.

FIG. 6(d) illustrates anticipatory deployment being changed when theoptimum distribution of autonomously driven vehicles is determined byfactoring in pricing data and analyzing one or more business variablescomprises determining a cost, where the dashed lines with associatedprobability values represent anticipated trips that had negative valuechanges in their variables.

In the illustration seen in FIG. 6(d) the anticipatory deployment isvisualized being changed when the optimum distribution of autonomouslydriven vehicles is determined by factoring in pricing data and analyzingone or more business variables comprises determining a cost, and wheretwo values have been recalculated to zero reflecting negative changes intheir variables.

A computerized system servicing any number of users and managing anynumber of autonomously driven vehicles can match users to vehicles usingany of a multitude of selection criteria as defined by the systemdesigners, users, and vehicle owners, including but not limited to;departure and arrival times, routes, type of vehicle, ride sharing, andany other criteria. FIG. 7 illustrates an example comprising multipleusers and multiple vehicles. In this illustration, the routes of threevehicles are shown over a 24 hour period, with ten routes, portions ofwhich are shown at 702, 703, 704 where the vehicles are providingtransportation (represented by the dashed lines such as 702, 703, 704),and nine periods and locations where the vehicles are stationary and areavailable to other users (represented by the elliptical shapes).

FIG. 8 illustrates the system response that may be generated to a userafter the user has submitted a request for transportation services tothe system. The system has calculated best matches to fulfill the userrequest using vehicles available based upon the groups the user is anowner/member of. The matches reflect the availability of vehicles thatmatch the time, route, personal preferences, and other search criteria,and the user is presented with a cost and service selection for thetrip.

FIGS. 9(a-b) illustrate operation of the anticipatory deployment systemin response to certain user commands. In the flowchart shown in FIGS.9(a-b), users may interface with other users on other electronic devicesto collaborate on their shared use of vehicles in any manner where thecollaboration can include ride sharing, schedule adjusting, routeadjusting, and any other method of modifying a travel event as a meansto facilitate the more efficient and cost effective anticipatorydeployment of autonomously controlled vehicles.

The method and system as described by the flow chart seen in FIGS.9(a-b) provides optimum distribution of autonomously driven vehicles,determined by factoring in pricing data obtained through any manner ofon-line, live, electronic marketplace, where the bidding for servicesoccurs between buyers and sellers. This includes any manner of matchingbuy and sell offers for services, and, factoring in pricing to includeusing real time pricing data generated from an electronic marketplace,to adjust the distribution of the vehicles in any manner, including toeffect financial results.

At step 902, a user enters a transportation request to the system, forexample, pick-up or drop-off at a specified location, within a specifiedtime window. “One-time preferences” are any trip specific requests orvariations from user profiles that the user requests for a trip.Example: most commonly you would assume people would like to shut offthe “will share a ride” preference on a trip for some personal reason.This way they won't be bothered by “will you share your ride” requestsfor that trip. The system retrieves at step 904, information pertainingto the user such as a user profile, a preference profile and theownership groups that the user belongs to. Examples of User Profilevariables include: (1) will you share your ride?; (2) who in your groupswill you share with and who will you exclude?; (3) what kind oftransportation are you looking to use; comfort, amenities, price?; (4)what are your favorite routes?.

The system then, step 906, determines the optimum route and a pluralityof alternate routes. Optimum route means the best match for all of thevariables (but especially price &, ride sharing), and, alternate choicesbased upon one variable being weighed more heavily. Example: choice Ameans leaving on time but paying $10 vs. alternate choice B meanswaiting 15 minutes but only paying $5. At step 908, the system checksfor ride sharing matches. The optimum and alternate routes determinedfor one user are compared to optimum and alternate routes determined forother users to identify overlaps, taking into account preferences fortimes and locations, and other variables specified by the users inquestion to calculate the routes and times that best match the userspecified needs within the system constraints. System constraintsinclude the number and size of the vehicles available in the groups ofwhich the users of interest are members. At step 910, the system checksfor vehicles available to service the requests in question and generatesroutes, which include start and end locations and path between the startand end locations, and times along with price, the price being thecurrency amount the program calculates as being attributed to a certaintransportation option, which might be calculated to include direct andindirect costs and costs attributable to user preferences such as,ride-sharing vs. riding alone, amenities in the vehicle such asentertainment features, the time the trip is initiated, the type ofvehicle, user requested variations to the route, and other costs thedesigners and operators of the system might choose to include. Thesystem at step 912 provides to the user the transportation options thatare available. The user at step 914 selects whether they are willing toshare a ride. The system at 916 determines the user's response and at918 responds by identifying and dispatching a vehicle for routing to theuser if the user selects a non-occupied vehicle and then returns to step908. If the user wishes to share a vehicle on the route in question withany other users, the system at step 920 communicates the ride sharerequest to the occupant(s) in the vehicle along with an offer of a priceadjustment. If the offer is accepted by the occupant(s) the system at922 routes the vehicle to pick up the user. Finally, at step 924 thesystem generates a financial accounting based upon the cost variablesthe system designers and operators have selected.

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

1-9: (canceled)
 10. A computer-accessible medium comprising programinstructions, wherein the program instructions are computer-executableto: determine the probability that users will require transportationservices at respective geographical areas at respective times by usinginput data including prior use patterns, user behaviors, and a pluralityof real-time input factors; determine hierarchical matrices ofgeographic locations and times by highest probability of users requiringtransportation services at respective geographical areas at respectivetimes; determine the optimum distribution of autonomously drivenvehicles by geographic locations and times so that vehicles can bepre-deployed to those geographic locations and times wheretransportation services are most anticipated to be needed.
 11. Thecomputer-accessible medium as recited in claim 10, wherein the optimumdistribution of autonomously driven vehicles is determined by: factoringin pricing data obtained via an on-line, live, electronic marketplacewhere bidding for services occurs between buyers and sellers and,factoring in pricing to include using real time pricing data generatedfrom an electronic marketplace, to adjust the distribution of thevehicles.
 12. The computer-accessible medium as recited in claim 10,wherein the optimum distribution of autonomously driven vehicles isdetermined by factoring in routing data obtained from user definedrouting data generated through a user interface on a digital media,including user defined routing data created to facilitate routepreferences, ride sharing, and flexible scheduling.
 13. Thecomputer-accessible medium as recited in claim 11, wherein factoring inpricing data comprises determining a cost associated with theanticipatory deployment of autonomous vhicles to provide transportationservices at the lowest cost. 14-18: (canceled)